What this category’s data looks like
Sources of consumer building materials investment research data include industry association monthly supply and demand monitoring reports, listed building materials enterprises’ quarterly and annual financial reports, offline dealers’ monthly inventory and sales ledgers, e-commerce platform consumer review data, and industry exhibition product parameter documents.
Update frequencies cover daily (public opinion), monthly (dealer ledgers, association reports), and quarterly/annual (financial reports).
Documents mostly use structured tables paired with text analysis. Single document lengths vary widely. Fields include product model, ex-factory price, regional sales volume, environmental protection grade, and others. Units include yuan per square meter, ton, cubic meter, and others.
Constraints for vector models and indexing workflows
High proportion of structured tables with multi-unit fields requires vector models to support structured field embedding, to avoid semantic deviation caused by unit confusion.
Coexisting data sources with multiple update frequencies requires indexing systems to support incremental updates, to balance retrieval efficiency and data freshness.
Wide variation in single document length requires chunk splitting logic to preserve the integrity of both long paragraphs and short table rows.
Mixed structure of short-text public opinion and long-text reports requires vector models to adapt to text embedding of different lengths, to avoid semantic truncation or redundancy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Consumer building materials documents have both long paragraph analysis and structured table rows. This range preserves semantic integrity for both, avoiding splitting that breaks table fields |
embedding_model | `bce-embedding-v1 | This model supports embedding multi-field structured data, and can adapt to semantic encoding of mixed fields such as building material product models, prices, and units |
recall_top_k | Top 8–12 results | Investment research scenarios need to cover multi-region, multi-model building material data. This recall volume avoids missing key information |
similarity_threshold | 0.72–0.78 | Building material product parameters have high similarity. This threshold range filters irrelevant results while retaining valid matches for same-category, different-model items |
index_refresh_interval | Triggered incrementally | Different data sources have large differences in update frequency. Incremental updates reduce invalid index execution and improve overall retrieval performance |
re_rank_top_k | Top 3–5 results | Investment research scenarios require precise matching of core parameters. Re-ranking reduces redundant results and focuses on key information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Each situation requires targeted analysis. Testing on local samples is recommended before finalizing the configuration.
Three common configuration errors
- After uploading chunk-mode data via the
pushdataAPI, the interface displays "Indexing" status for an extended period. The cause is thatindex_refresh_intervalis not set to incremental trigger, leading to blocking from repeated full index execution. - Retrieval results have low relevance, with all results showing similar similarity scores. The cause is that structured field embedding is not enabled, and only plain text content is encoded, unable to match key parameters such as building material product models and prices.
- The final set of indexes fails verification after question answering splitting. The cause is that chunk splitting does not preserve complete table rows, with the final set of chunks only containing unit fields. Insufficient semantic information causes vector embedding failure.
How to confirm correct configuration
- Upload a single consumer building materials monthly supply and demand report, perform a retrieval test, and verify that returned results include matching content for specified fields such as product model and regional sales volume.
- View index execution logs, confirm that incremental indexing is only triggered after new data is submitted, with no records of repeated full index execution.
- Adjust the value range of the similarity threshold, and verify that the number of recalled results adapts to the information needs of the investment research scenario.
- Upload a single piece of structured building material product parameter data, and confirm that it can be accurately recalled after vector embedding.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.